


%TABLE 5 - STATE REGRESSIONS


clear all;

B = dlmread('stateweights.dat')
ii=B(:,1);
jj=B(:,2);
ss=B(:,3);
clear B;
E=sparse(ii,jj,ss,19,19);
clear ii; clear jj; clear ss;
A = dlmread('sthurr_c_st.dat');


% fixed effects spatial autocorrelation program written by: J.Paul Elhorst 9/2004
% University of Groningen
% Department of Economics
% 9700AV Groningen
% the Netherlands
% j.p.elhorst@eco.rug.nl
%
% dimensions of the problem
T=144; % number of time periods
N=19; % number of regions
nobs=N*T;
% row-normalize W
W=normw(E); % function of LeSage

% Table 5 - Column (1)
y=A(:,[3]); % column number in the data matrix that corresponds to the dependent variable
x=A(:,[4,5,6,7,8]); % column numbers in the data matrix that correspond to the independent variables
xconstant=ones(nobs,1);
info.lflag=0;
info.model=3;
results=sem_panel(y,x,W,T,info);
vnames=strvcat('growth','initial','hurr','hurr1','hurr2','hurr3');
prt_sp(results,vnames,1);

%SHORT TERM COUNTY
clear all;

B = dlmread('stateweights.dat')
ii=B(:,1);
jj=B(:,2);
ss=B(:,3);
clear B;
E=sparse(ii,jj,ss,19,19);
clear ii; clear jj; clear ss;
A = dlmread('sthurrb.dat');


% dimensions of the problem
T=144; % number of time periods
N=19; % number of regions
nobs=N*T;
% row-normalize W
W=normw(E); % function of LeSage

% Table 5 - Column (2)
y=A(:,[3]); % column number in the data matrix that corresponds to the dependent variable
x=A(:,[4,5,6,7,8]); % column numbers in the data matrix that correspond to the independent variables
xconstant=ones(nobs,1);
info.lflag=0;
info.model=3;
results=sem_panel(y,x,W,T,info);
vnames=strvcat('growth','initial','hurr','hurr1','hurr2','hurr3');
prt_sp(results,vnames,1);

%Long Term
clear all;

B = dlmread('stateweights.dat')
ii=B(:,1);
jj=B(:,2);
ss=B(:,3);
clear B;
E=sparse(ii,jj,ss,19,19);
clear ii; clear jj; clear ss;
A = dlmread('sthurr_bc_lt.dat');

% dimensions of the problem
T=231; % number of time periods
N=19; % number of regions
nobs=N*T;
% row-normalize W
W=normw(E); % function of LeSage

% Table 5 - Column (3)
y=A(:,[3]); % column number in the data matrix that corresponds to the dependent variable
x=A(:,[4,5,6,7,8]); % column numbers in the data matrix that correspond to the independent variables
xconstant=ones(nobs,1);
info.lflag=0;
info.model=3;
results=sem_panel(y,x,W,T,info);
vnames=strvcat('growth','initial','hurr','hurr1','hurr2','hurr3');
prt_sp(results,vnames,1);